The growing popularity of mobile services has resulted in ever-increasing interference levels caused by the closer proximity of users, and in the case of time division multiple access (TDMA) based systems by a tighter frequency reuse. As a result, mutual interference among users occupying the same radio channel has become a major source of signal disturbance. The ability to suppress co-channel interference has become increasingly important for mobile receivers in cellular systems with tight reuse. This has led to the development of several techniques for interference suppression in the receiver units of the base transceiver stations (BTS) or mobile stations (MS).
Multi-branch diversity or array processing is a class of commonly used techniques for suppressing interference, in which multiple versions of the same transmitted signal are produced and processed jointly in the receiver in order to cancel one or more interfering signals. The different signal versions may be obtained by using multiple receiving antennas, by sampling the received signal over the baud rate of transmission (i.e. over sampling), by separating in-phase (I) and quadrature-phase  of the signal, or by combinations of these. The method of separating in-phase and quadrature-phase of the signal is commonly referred to as single-antenna-interference cancellation (SAIC) and has recently received much attention in the so called GERAN standardization.
In conventional array processing, the interference is typically modeled as temporally (across time) and/or spatially (across different signal versions) colored noise. By performing proper spatial and/or temporal noise whitening, the interference can be suppressed substantially. Such whitening operation may be performed before or during demodulation/equalization.
In order to suppress the noise or interference through spatial-temporal whitening, the receiver typically requires an estimate of a certain spectral property of the noise, such as the noise covariance matrix. From such spectral property, a whitening filter can then be derived to whiten, and therefore suppress, the noise. If the statistics of interference can be assumed to be approximately stationary over the data burst, the estimation of the noise spectral property may be performed over a sequence of training symbols in each data burst that is known to the receiver.
In addition, the demodulator or equalizer of the receiver must also be able to synchronize to the beginning of a data burst in order to begin demodulation. The synchronization process is typically done jointly with channel estimation over the training sequence. When spatial/temporal whitening is performed on the received signal to suppress noise or interference, the operating carrier-to-interference power ratio (C/I) can be changed so drastically that the ordinary method of synchronization and channel estimation, such as the least squares (LS) method, can no longer produce an accurate synchronization position. As a result, the reliability of synchronization and channel estimation becomes a bottleneck of the overall receiver performance.
One known way of improving synchronization and quality of channel estimation in a multi-branch receiver is to first perform a certain initial synchronization and channel estimation, such as the LS channel estimation, and then estimate the noise covariance matrix or function based on the residual signal after channel estimation. From the estimated noise covariance matrix, a whitening filter can be computed using the well-known Whittle-Wiggins-Robinson Algorithm (WWRA). The problem with this approach is that the initial synchronization and channel estimation (before whitening) may not produce an accurate estimate of the synchronization position and the channel estimate. As a result, the statistics of the residual signal obtained from the initial synchronization and channel estimation may not be representative of the statistics of the actual noise or interference.
To overcome this, one known technique [1] is the so called Indirect Spatio-Temporal Interference Rejection Combining (Indirect ST IRC), which is a joint synchronization, channel estimation and noise covariance estimation technique. The use of this technique in the receiver algorithms for BTS or MS results in substantial interference suppression.
The technique described in [1] gives a method to jointly estimate the synchronization position, channel, and noise covariance matrix, given a baseband model for a received signal containing a known training sequence. However, the length of the channel and the dimension of the noise covariance matrix are assumed to be known. The choice of the channel length and the dimension of the noise covariance matrix will be referred to as the model order selection problem in the following detailed description.
Existing solutions to the model order selection problem can be divided into two groups. In the first group, the order of the model is fixed, and can be guessed or deduced from field measurements and subsequently hard coded into the algorithms. In the second group there are the ad hoc methods based on simulations. In this methodology, a statistical regression is used to produce a table. The regression is made from simulation-generated data.
Neither of these two groups is satisfactory. The main disadvantage of choosing a fixed model order is that it lacks the flexibility needed to cope with the diverse deployment scenarios found in mobile networks. The main disadvantage of the ad hoc methods is that the mobile system may be put to work in environments that do not necessarily fit the simulation conditions or the test cases chosen by the system designers.
Therefore, there is a need for improved methods and arrangements for model order selection to enable improved ST IRC.